MOTE (version 1.0.2)

d.dep.t.diff.t: d from t for Repeated Measures with SD Difference Scores Denominator

Description

This function displays d for repeated measures data and the non-central confidence interval using the standard deviation of the differences as the denominator estimating from the t-statistic.

Usage

d.dep.t.diff.t(t, n, a = 0.05)

Arguments

t

t-test value

n

sample size

a

significance level

Value

d

effect size

dlow

lower level confidence interval d value

dhigh

upper level confidence interval d value

n

sample size

df

degrees of freedom (sample size - 1)

p

p-value

estimate

the d statistic and confidence interval in APA style for markdown printing

statistic

the t-statistic in APA style for markdown printing

Details

To calculate d, the t-statistic is divided by the square root of the sample size.

d_z = t / sqrt(n)

Learn more on our example page.

Examples

# NOT RUN {
#The following example is derived from the "dept_data" dataset included
#in the MOTE library.

#In a study to test the effects of science fiction movies on people<U+2019>s belief
#in the supernatural, seven people completed a measure of belief in
#the supernatural before and after watching a popular science
#fiction movie. Higher scores indicated higher levels of belief.

    scifi = t.test(dept_data$before, dept_data$after, paired = TRUE)

#The t-test value was 1.43. You can type in the numbers directly,
#or refer to the dataset, as shown below.

    d.dep.t.diff.t(t = 1.43, n = 7, a = .05)

    d.dep.t.diff.t(1.43, 7, .05)

    d.dep.t.diff.t(scifi$statistic, length(dept_data$before), .05)

#The mean measure of belief on the pretest was 5.57, with a standard
#deviation of 1.99. The posttest scores appeared lower (M = 4.43, SD = 2.88)
#but the dependent t-test was not significant using alpha = .05,
#t(7) = 1.43, p = .203, d_z = 0.54. The effect size was a medium effect suggesting
#that the movie may have influenced belief in the supernatural.
# }